Description

This course aims to provide a comprehensive introduction to machine learning, with a primary focus on classification and regression approaches and the methodologies to apply them in practice. The main topics covered include:

- Data processing

- Supervised methods (decision tree, SVM, ...)

- Unsupervised methods (Kmeans, HAC, ...)

- Neural networks supervised and unsupervised

- Evaluation methods supervised (F1-score, accuracy, ...) and unsupervised (ARI, NMI, ...) 

Compétences requises

Write simple Python programs

Compétences visées

• Process the data to be used in a learning task

• Explain the principles of the main supervised and unsupervised methods

• Implement and use these methods

• Evaluate a learning result with the right tools, depending on the target objective

Disciplines

• Informatique

Bibliographie

• Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Contacts

Responsable(s) de l'enseignement

• Baptiste Lafabregue

MCC

Les épreuves indiquées respectent et appliquent le règlement de votre formation, disponible dans l'onglet Documents de la description de la formation.

Régime d'évaluation

ECI (Évaluation continue intégrale)

Coefficient

1.0

Évaluation initiale / Session principale - Épreuves